LI Shuzhou

Appointment: Associate Professor, School of Material Science & Engineering

Assistant Chair, Graduate Studies and Lifelong Learning

Research topics:

• developing high sensitive substrates for surface-enhanced Raman scattering and fluorescence

• optical properties of metal-semiconductor nanostructures

• assembly of metallic nanostructures

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71 to 80 of 1,773 Results

pr_all.edr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 207.4 KB - MD5: af08413803b0911235e9853485a08c76
pr_all.edr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 2.1 MB - MD5: 3adca7ebc8db212de40723eaa0817948
pr_all.gro Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 27.3 MB - MD5: f36d8f4f477d4f51a8d64773f558a609
pr_all.gro Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 12.7 MB - MD5: bf78f61cf10ed58c2f1083cd14b7799e
pr_all.log Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Plain Text - 2.5 MB - MD5: 043d8fef5c5e6b9e0b9f3845b261c8a7
pr_all.log Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Plain Text - 262.3 KB - MD5: 9211beeee5a9f6438e038eb1216acee2
pr_all_prev.cpt Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning application/mac-compactpro - 9.4 MB - MD5: 5692e829af1c9938fdd035b744a72482
pr_all_prev.cpt Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning application/mac-compactpro - 4.3 MB - MD5: fa1db8c7f6de83f9f73b9c7978fb4e4a
pr_all.tpr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 9.5 MB - MD5: d66bc3a30223c12959d409469764b8f1
pr_all.tpr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 4.6 MB - MD5: 49020a3df919513af432259efdf0e7c7

pr_all.edr

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Unknown - 207.4 KB -

pr_all.edr

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Unknown - 2.1 MB -

pr_all.gro

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Unknown - 27.3 MB -

pr_all.gro

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Unknown - 12.7 MB -